人工智能
拉曼光谱
卷积神经网络
机器学习
计算机科学
分析物
二元分类
拉曼散射
模式识别(心理学)
二进制数
鉴定(生物学)
材料科学
化学
支持向量机
色谱法
物理
数学
光学
算术
生物
植物
作者
Travon Cooman,Colby E. Ott,Luis E. Arroyo‐Mora
标识
DOI:10.1111/1556-4029.15285
摘要
Multiple analytical techniques for the screening of fentanyl-related compounds exist. High discriminatory methods such as GC-MS and LC-MS are expensive, time-consuming, and less amenable to onsite analysis. Raman spectroscopy provides a rapid, inexpensive alternative. Raman variants such as electrochemical surface-enhanced Raman scattering (EC-SERS) can provide signal enhancements with 1010 magnitudes, allowing for the detection of low-concentration analytes, otherwise undetected using conventional Raman. Library search algorithms embedded in instruments utilizing SERS may suffer from accuracy when multicomponent mixtures involving fentanyl derivatives are analyzed. The complexing of machine learning techniques to Raman spectra demonstrates an improvement in the discrimination of drugs even when present in multicomponent mixtures of various ratios. Additionally, these algorithms are capable of identifying spectral features difficult to detect by manual comparisons. Therefore, the goal of this study was to evaluate fentanyl-related compounds and other drugs of abuse using EC-SERS and to process the acquired data using machine learning-convolutional neural networks (CNN). The CNN was created using Keras v 2.4.0 with Tensorflow v 2.9.1 backend. In-house binary mixtures and authentic adjudicated case samples were used to evaluate the created machine-learning models. The overall accuracy of the model was 98.4 ± 0.1% after 10-fold cross-validation. The correct identification for the in-house binary mixtures was 92%, while the authentic case samples were 85%. The high accuracies achieved in this study demonstrate the advantage of using machine learning to process spectral data when screening seized drug materials comprised of multiple components.
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